There is a substantial amount of research in RL to find the optimal policy. A few examples are: policy gradient methods, which solve for the policy directly [1]}, [2]}, Q-Learning that solve for the optimal Q-Function [3]}, [4]}, and actor-critic methods which find both [5]}, [6]}. OpenAI also created a well established standard in the RL community for developing new environments to allow for easier evaluation of RL algorithms [7]}. By creating syntatic parallels, the state-of-art in RL may be directly applied to surgical robot platforms via dVRL.
